deepctr_torch.models.autoint module

Author:
Weichen Shen,weichenswc@163.com
Reference:
[1] Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.(https://arxiv.org/abs/1810.11921)
class deepctr_torch.models.autoint.AutoInt(linear_feature_columns, dnn_feature_columns, att_layer_num=3, att_head_num=2, att_res=True, dnn_hidden_units=(256, 128), dnn_activation='relu', l2_reg_dnn=0, l2_reg_embedding=1e-05, dnn_use_bn=False, dnn_dropout=0, init_std=0.0001, seed=1024, task='binary', device='cpu', gpus=None)[source]

Instantiates the AutoInt Network architecture.

Parameters:
  • linear_feature_columns – An iterable containing all the features used by linear part of the model.
  • dnn_feature_columns – An iterable containing all the features used by deep part of the model.
  • att_layer_num – int.The InteractingLayer number to be used.
  • att_head_num – int.The head number in multi-head self-attention network.
  • att_res – bool.Whether or not use standard residual connections before output.
  • dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of DNN
  • dnn_activation – Activation function to use in DNN
  • l2_reg_dnn – float. L2 regularizer strength applied to DNN
  • l2_reg_embedding – float. L2 regularizer strength applied to embedding vector
  • dnn_use_bn – bool. Whether use BatchNormalization before activation or not in DNN
  • dnn_dropout – float in [0,1), the probability we will drop out a given DNN coordinate.
  • init_std – float,to use as the initialize std of embedding vector
  • seed – integer ,to use as random seed.
  • task – str, "binary" for binary logloss or "regression" for regression loss
  • device – str, "cpu" or "cuda:0"
  • gpus – list of int or torch.device for multiple gpus. If None, run on device. gpus[0] should be the same gpu with device.
Returns:

A PyTorch model instance.
forward(X)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.